Asynchronous State Estimation Research Articles

Non-fragile mixed H∞ and passive asynchronous state estimation for Markov jump neural networks with randomly occurring uncertainties and sensor nonlinearity

This paper is concerned with the non-fragile mixed H∞ and passive asynchronous state estimation problem for uncertain discrete-time Markov jump neural networks (MJNNs). Both the uncertainties of system and the sensor nonlinearity are considered to be randomly occurring which are governed by a set of Bernoulli distributed white sequences. Since inaccuracies or uncertainties may occur in the designed state estimator and the complete mode synchronization between plant and state estimator is hardly possible, a non-fragile asynchronous state estimator design method is presented. By using an optimize matrix decoupling approach and Lyapunov-Krasovskii methodology, some sufficient conditions for the existence of non-fragile mixed H∞ and passive asynchronous state estimator are proposed. A numerical example is presented to demonstrate the effectiveness of our proposed method.

Asynchronous Dissipative State Estimation for Stochastic Complex Networks With Quantized Jumping Coupling and Uncertain Measurements.

This paper addresses the problem of state estimation for a class of discrete-time stochastic complex networks with a constrained and randomly varying coupling and uncertain measurements. The randomly varying coupling is governed by a Markov chain, and the capacity constraint is handled by introducing a logarithmic quantizer. The uncertainty of measurements is modeled by a multiplicative noise. An asynchronous estimator is designed to overcome the difficulty that each node cannot access to the coupling information, and an augmented estimation error system is obtained using the Kronecker product. Sufficient conditions are established, which guarantee that the estimation error system is stochastically stable and achieves the strict (Q, S, R)-γ-dissipativity. Then, the estimator gains are derived using the linear matrix inequality method. Finally, a numerical example is provided to illustrate the effectiveness of the proposed new design techniques.

Sequential Asynchronous Filters for Target Tracking in Wireless Sensor Networks

Asynchronous data fusion is inevitable for target tracking in asynchronous wireless sensor networks, where multiple sensors are required to locate a target collaboratively. The predicted estimates of the follow-up states based on the to-be-estimated state are first introduced to overcome the drawback that asynchronous measurements cannot be fused directly. Then, the sequential asynchronous Bayesian state estimation is deduced based on the predicted estimates. The proposed estimation process is comprised of two steps: 1) the prediction step and 2) the update step. Finally, sequential asynchronous filters based Kalman filter and particle filter are proposed. Simulations demonstrate that the proposed algorithms perform not only better than the benchmark algorithms with asynchronous measurements, but also better than the benchmark algorithm with synchronous measurements.

Asynchronous distributed state estimation for continuous-time stochastic processes

The problem of state estimation of a continuous-time stochastic process using an Asynchronous Distributed multi-sensor Estimation (ADE) system is considered. The state of a process of interest is estimated by a group of local estimators constituting the proposed ADE system. Each estimator is based, e.g., on a Kalman filter and performs single sensor filtration and fusion of its local results with the results from other/remote processors to compute possibly the best state estimates. In performing data fusion, however, two important issues need to be addressed namely, the problem of asynchronism of local processors and the issue of unknown correlation between asynchronous data in local processors. Both the problems, along with their solutions, are investigated in this paper. Possible applications and effectiveness of the proposed ADE approach are illustrated by simulated experiments, including a non-complete connection graph of such a distributed estimation system.

Asynchronous distributed state estimation based on a continuous‐time stochastic model

SUMMARYIn this paper, the problem of state estimation in an asynchronous distributed multi‐sensor estimation (ADE) system is considered. In such an ADE system, the state of a plant of interest is estimated by a group of local estimators. Each local estimator based, for example, on a Kalman filter, performs fusion of data from its local sensor and other (remote) processors to compute possibly best state estimates. In performing data fusion, however, two important issues need to be addressed, namely, the problem of asynchronism of local processors and the one of unknown correlation between asynchronous data in local processors. Consequently, there are two main contributions proposed in this paper. The first is a method to deal with asynchronous discrete‐time data based on a continuous‐time stochastic plant model. The second contribution is an asynchronous distributed data‐fusion algorithm. Simulated experiments illustrate the effectiveness of the proposed ADE approach. Copyright © 2011 John Wiley & Sons, Ltd.